Speech recognition applications have evolved significantly during recent years. Nowadays the performance enables recognition, which is much faster than real-time human speech, and the accuracy is near to human being level. In many applications, the accuracy is only limited due to the fact the application requires user-specific training data, i.e. the accuracy may be worse for the voice of a non-user.
Various devices, like mobile terminals, include speech recognition applications facilitating the use of the device. For example, there are speech recognition applications, which enable to open a specific application (e.g. a calendar or contacts) by saying aloud known keywords and pressing a certain key of the keypad at the same time.
Currently during the phone conversation, the end users sometimes have to interrupt the conversation, open a new application manually and check some info which is already available on the mobile devices. For example, when making an appointment, the end user needs to check if he/she is available on the proposed date, or the end user needs to check where is the good place to meet for both parties involved in the conversation, or the end user needs to check if he/she has the contact information to involve a third party for the appointment or sending information to him/her.
Thus, despite of the fact that all the required context information such as time, location, and contacts, is already available on the mobile device, the user still have to manually open applications. This is an inconvenient and cumbersome process in terms of the usability of the mobile device.
Accordingly, there is a need for an enhanced method for retrieving the context information easily for the end user during the phone conversation.